At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Alex was a Fellow in our Fall 2015 cohort in Washington, DC who landed a job with our hiring partner, NAUTO, in Palo Alto, California.

Tell us about your background. How did it set you up to be a great data scientist?

I went in a straight line for 28 years, and then zig-zagged all over the place. I pursued and received a PhD in Math from UCLA, which culminated two decades of focusing on math. However, during my grad studies I developed other interests, and following grad school I did a lot of political activism and founded a not-for-profit bicycle shop. After that I worked in K-12 Education for 3.5 years, first at Green Dot Public Schools, then at McGraw Hill Education. That gave me a lot of business experience that has proved to be useful connecting the technical side of data science with the business side.

What do you think you got out of The Data Incubator?

It helped me get from the stage of unconscious incompetence – not knowing what you don’t know about data science – to conscious incompetence – knowing what you don’t know, and knowing how to fix that. After five hard weeks of homework, you have some pretty good skills, but more importantly, you have a good idea of where you need to spend time learning, and how to learn. If I was an employer, I would feel comfortable hiring people who have been through The Data Incubator, since they are (a) accomplished hard workers and (b) have shown a willingness and ability to learn a very new field.Continue reading →

One of our fellows recently had a piece published about her very unique and timely capstone project. The original piece is posted on Data Driven Journalism.

In her own words:

This war is not only important due to its staggering costs (both human and financial) but also on account of its publicly available and well-documented daily records from 2004 to 2010.

These documents provide a very high spatial and temporal resolution view of the conflict. For example, I extracted from these government memos the number of violent events per day in each county. Then, using latent factor analysis techniques, e.g. non-negative matrix factorization, I was able to cluster the top three principal war zones. Interestingly these principal conflict zones were areas populated by the three main ethno-religious groups in Iraq.